THIS IS A COVID-19 DATA VISUALIZATION PROJECT

Here we will use some pyhton libraries and analyze the Covid-19 pandemic and its effect in various countries globally

In case of any query you can contact me via gmail: amitabhdeb.nits@gmail.com

#!pip install folium
#!pip install plotly

#imports
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots

import folium


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline

import math
import random
from datetime import timedelta

import warnings
warnings.filterwarnings('ignore')

#color_pallettes
cnf = '#393e46'
dth = '#ff2e63'
rec = '#21bf73'
act = '#fe9801'

Data Preparation

import plotly as py
py.offline.init_notebook_mode(connected=True)

clean_data = pd.read_csv('C:/Users/studd/Downloads/JOB/projects/Covid-19 project/Covid-19-Preprocessed-Dataset-master/Covid-19-Preprocessed-Dataset-master/preprocessed/covid_19_data_cleaned.csv', parse_dates=['Date'])


country_daywise = pd.read_csv('C:/Users/studd/Downloads/JOB/projects/Covid-19 project/Covid-19-Preprocessed-Dataset-master/Covid-19-Preprocessed-Dataset-master/preprocessed/country_daywise.csv', parse_dates=['Date'])


country_wise = pd.read_csv('C:/Users/studd/Downloads/JOB/projects/Covid-19 project/Covid-19-Preprocessed-Dataset-master/Covid-19-Preprocessed-Dataset-master/preprocessed/countrywise.csv')


daywise = pd.read_csv('C:/Users/studd/Downloads/JOB/projects/Covid-19 project/Covid-19-Preprocessed-Dataset-master/Covid-19-Preprocessed-Dataset-master/preprocessed/daywise.csv', parse_dates=['Date'])

clean_data['Province/State'] = clean_data['Province/State'].fillna("")
clean_data.head()

	Date	Province/State	Country	Lat	Long	Confirmed	Recovered	Deaths	Active
0	2020-01-22		Afghanistan	33.93911	67.709953	0	0	0	0
1	2020-01-23		Afghanistan	33.93911	67.709953	0	0	0	0
2	2020-01-24		Afghanistan	33.93911	67.709953	0	0	0	0
3	2020-01-25		Afghanistan	33.93911	67.709953	0	0	0	0
4	2020-01-26		Afghanistan	33.93911	67.709953	0	0	0	0

confirmed = clean_data.groupby('Date').sum()['Confirmed'].reset_index()
recovered = clean_data.groupby('Date').sum()['Recovered'].reset_index()
deaths = clean_data.groupby('Date').sum()['Deaths'].reset_index()

#clean_data.info()

clean_data.query('Country=="India"').tail()

	Date	Province/State	Country	Lat	Long	Confirmed	Recovered	Deaths	Active
77547	2021-06-24		India	20.593684	78.96288	30134445	29128267	393310	612868
77548	2021-06-25		India	20.593684	78.96288	30183143	29193085	394493	595565
77549	2021-06-26		India	20.593684	78.96288	30233183	29251029	395751	586403
77550	2021-06-27		India	20.593684	78.96288	30279331	29309607	396730	572994
77551	2021-06-28		India	20.593684	78.96288	30279331	29309607	396730	572994

Scatter plot of confirmed,recovered and death cases Worldwide

fig = go.Figure()
fig.add_trace(go.Scatter(x = confirmed['Date'], y = confirmed['Confirmed'], mode = 'lines+markers', name = 'Confirmed Cases', line = dict(color = "Red", width = 0.5)))
fig.add_trace(go.Scatter(x = recovered['Date'], y =recovered['Recovered'], mode = 'lines+markers', name = 'Recovered Cases', line = dict(color = "Green", width = 0.5)))
fig.add_trace(go.Scatter(x = deaths['Date'], y = deaths['Deaths'], mode = 'lines+markers', name = 'Deaths Cases', line = dict(color = "Black", width = 0.5)))
fig.update_layout(title = 'WorldWide Covid-19 Cases', yaxis = dict(title = 'Number of Cases'))

fig.show()

Case Density Animation on World Map

clean_data['Date'] = clean_data['Date'].astype(str)
#clean_data.info()

fig = px.density_mapbox(clean_data, lat = 'Lat', lon = 'Long', hover_name = 'Country', hover_data = ['Confirmed', 'Recovered', 'Deaths'], animation_frame= 'Date', color_continuous_scale='Portland', radius=7, zoom=0, height=700)
fig.update_layout(title = 'WorldWide Covid-19 Cases with Time Laps')
fig.update_layout(mapbox_style = 'open-street-map', mapbox_center_lon = 0)

fig.show()

Cases over the time with Area Plot

temp = clean_data.groupby('Date')['Recovered','Active', 'Deaths'].sum().reset_index()
temp = temp.melt(id_vars='Date', value_vars=['Deaths', 'Recovered','Active'], var_name='Case', value_name='Count')

fig = px.area(temp, x = 'Date', y = 'Count', color = 'Case', height = 600, title = 'Cases Over Time', color_discrete_sequence=[dth, rec, act])
fig.update_layout(xaxis_rangeslider_visible = True)
fig.show()

Top 15 Covid affected countries Case Analysis

top = 15

fig_c = px.bar(country_wise.sort_values('Confirmed').tail(top), x = 'Confirmed', y = 'Country', text = 'Confirmed', orientation = 'h', color_discrete_sequence = [cnf])
fig_a = px.bar(country_wise.sort_values('Active').tail(top), x = 'Active', y = 'Country', text = 'Active', orientation = 'h', color_discrete_sequence = [act])
fig_r = px.bar(country_wise.sort_values('Recovered').tail(top), x = 'Recovered', y = 'Country', text = 'Recovered', orientation = 'h', color_discrete_sequence = [rec])
fig_d = px.bar(country_wise.sort_values('Deaths').tail(top), x = 'Deaths', y = 'Country', text = 'Deaths', orientation = 'h', color_discrete_sequence = [dth])

fig = make_subplots(rows=1, cols=4, shared_xaxes=False, horizontal_spacing=0.14, vertical_spacing=0.1, subplot_titles=('Confirmed Cases', 'Active Cases', 'Recovered Cases', 'Death Cases'))


fig.add_trace(fig_c['data'][0], row = 1, col=1)
fig.add_trace(fig_a['data'][0], row = 1, col=2)
fig.add_trace(fig_r['data'][0], row = 1, col=3)
fig.add_trace(fig_d['data'][0], row = 1, col=4)

fig.update_layout(height = 500)
fig.show()

Scatter Plot for Death vs Confirmed Cases(for top 15 countries)

top = 15
fig = px.scatter(country_wise.sort_values('Deaths', ascending=False).head(top), x = 'Confirmed', y = 'Deaths', color='Country',
                size = 'Confirmed', height = 700, text = 'Country', log_x=True, log_y=True, title='Death vs Confirmed cases on Log10 scale')

fig.update_traces(textposition = 'top center')
fig.update_layout(showlegend = True, xaxis_rangeslider_visible = True)
fig.show()

Tree Map Analysis

full_latest = clean_data[clean_data['Date'] == max(clean_data['Date'])]

fig = px.treemap(full_latest.sort_values(by = 'Confirmed', ascending=False).reset_index(drop = True), path = ['Country'], values='Confirmed',
                height = 700, title='Number of Confirmed Cases', color_discrete_sequence=px.colors.qualitative.Dark2)

fig.data[0].textinfo = 'label+text+value'
fig.show()

full_latest = clean_data[clean_data['Date'] == max(clean_data['Date'])]

fig = px.treemap(full_latest.sort_values(by = 'Deaths', ascending=False).reset_index(drop = True), path = ['Country'], values='Deaths',
                height = 700, title='Number of Death Cases', color_discrete_sequence=px.colors.qualitative.Dark2)

fig.data[0].textinfo = 'label+text+value'
fig.show()

Covid-19 vs Other Similar Epidemics

The below data is taken from wikipedia

epidemics = pd.DataFrame({
    'epidemic' : ['SARS', 'H1N1', 'MERS', 'EBOLA', 'COVID-19'],
    'start_year' : [2002, 2009, 2012, 2013, 2019],
    'end_year' : [2004, 2010, 2020, 2016, 2021],
    'confirmed' : [8422, 6724149, 2519, 28646, full_latest['Confirmed'].sum()],
    'deaths' : [813, 19654, 866, 11323, full_latest['Deaths'].sum()]
})

epidemics['mortality'] = round((epidemics['deaths']/epidemics['confirmed'])*100,2)
epidemics.head()

	epidemic	start_year	end_year	confirmed	deaths	mortality
0	SARS	2002	2004	8422	813	9.65
1	H1N1	2009	2010	6724149	19654	0.29
2	MERS	2012	2020	2519	866	34.38
3	EBOLA	2013	2016	28646	11323	39.53
4	COVID-19	2019	2021	181388210	3928804	2.17

temp = epidemics.melt(id_vars='epidemic', value_vars=['confirmed', 'deaths', 'mortality'], var_name= 'Case', value_name='Value')

fig = px.bar(temp, x = 'epidemic', y = 'Value', color='epidemic', text= 'Value', facet_col='Case', color_discrete_sequence=px.colors.qualitative.Bold)

fig.update_traces(textposition='outside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode = 'hide')
fig.update_yaxes(showticklabels=False)
fig.layout.yaxis2.update(matches=None)
fig.layout.yaxis3.update(matches=None)
fig.show()

With this, we have come to the end of this Data Visualization project on Covid-19. In this project we tried to observe the effect of Covid on various countries all over the world using python and its libraries.